Adjusting for Unit Nonresponse That May Not be Missing at Random

调整可能不会随机丢失的单位无反应

• 批准号: 1424492
• 负责人: Phillip Kott
• 金额: $ 14.5万
• 依托单位: Research Triangle Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1424492&HistoricalAwards=false
• 关键词: Adjusting; Unit; Nonresponse; May; Missing

This research project will further understanding of a statistical technique for measuring and reducing the potential for biases from non-response in sample surveys. Sample surveys are a principal tool for providing accurate information crucial for good economic and social decision making. Nonresponse in sample surveys conducted both privately and by government agencies is increasing. With that increase, the potential for serious biases in estimates derived from surveys and used in critical decision making grows. This research will address that problem, particularly in cases where non-response may be a function of variables having missing values. Extensions of this research will have broad application to multi-phase surveys, non-probability surveys, and other statistical applications plagued by potential selection bias whether due to nonresponse, coverage errors, or both. The project will develop powerful, publically available tools for implementing this approach.Calibration weighting can be used to remove potential selection biases due to unit non-response or coverage errors. This research project will demonstrate the advantages in terms of reductions in non-response bias and increases in accuracy of having more calibration than model variables when non-respondents are determined not to be missing at random. In so doing, it will introduce a better method of calibration weighting in this context than currently exists in the literature. The research also will investigate new tests for determining whether a set of calibration variables can by itself serve as the model variables or whether a survey variable truly needs to be added to the response model to avoid selection bias. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

该研究项目将进一步了解用于衡量和减少抽样调查中不答复造成的潜在偏差的统计技术。 抽样调查是提供准确信息的主要工具，而准确信息对良好的经济和社会决策至关重要。 在私人和政府机构进行的抽样调查中，不作答复的现象正在增加。 随着这一增长，从调查中得出的估计和用于关键决策的估计出现严重偏差的可能性也在增加。 这项研究将解决这一问题，特别是在无响应可能是具有缺失值的变量的函数的情况下。 这项研究的扩展将广泛应用于多阶段调查，非概率调查，以及其他受潜在选择偏差困扰的统计应用，无论是由于无响应，覆盖误差，还是两者兼而有之。 该项目将开发强大的，可用于实现这种方法的工具。校准加权可以用来消除潜在的选择偏差，由于单位无响应或覆盖错误。 本研究项目将展示在减少无应答偏倚方面的优势，以及当确定非应答者不是随机缺失时，比模型变量具有更多校准的准确性增加。 在这样做的时候，它将介绍一个更好的方法，校准加权在这种情况下，比目前存在的文献。 该研究还将调查新的测试，以确定是否一组校准变量本身可以作为模型变量，或者是否真的需要将调查变量添加到响应模型中以避免选择偏差。 该项目得到了方法、测量和统计方案以及联邦统计机构联合会的支持，作为支持调查和统计方法研究的联合活动的一部分。